Sentential context, which involves deep syntactic and semantic structure of the source and target languages [1]}, is crucial for machine translation. In statistical machine translation (SMT), the sentential context has proven beneficial for predicting local translations [2]}, [3]}. The exploitation of sentential context in neural machine translation  [4]}, however, is not well studied. Recently, C18-1276 showed that the translation at each time step should be conditioned on the whole target-side context. They introduced a deconvolution-based decoder to provide the global information from the target-side context for guidance of decoding.
